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Test_captions.py
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Test_captions.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 17 14:41:19 2022
@author: gaurav
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from pickle import load
import tensorflow as tf
import numpy as np
from My_XF import Mem_Transformer, create_masks
data_path = './SCAMET/'
tokenizer = load(open(data_path+'/data_preprocess/sc_tokenizer.p',"rb"))
vocab_size = len(tokenizer.word_index)
max_len = 23
batch_size = 128
num_layers = 3
d_model = 512
buffer_size = 512
dff = d_model*4
num_heads = 8
target_vocab_size = vocab_size
input_vocab_size = target_vocab_size
dropout_rate = 0.2
EMB_MAT=None
num_memory = 30
transformer = Mem_Transformer(num_memory, num_layers, d_model, EMB_MAT, num_heads, dff, input_vocab_size, target_vocab_size, pe_input=max_len, pe_target=target_vocab_size, rate=dropout_rate)
img_feature_path = data_path+'/Sydney_captions/Img_features/'
chkpt_path = data_path+'/Chk_Path/EN_3DE'
chkpt = tf.train.Checkpoint(transformer=transformer)
chkpt_manager = tf.train.CheckpointManager(chkpt, chkpt_path, max_to_keep=5)
chkpt.restore(chkpt_manager.latest_checkpoint)
def feat_evaluate(test_img_n):
img_id = test_img_n.split('/')[-1]
img_feat= np.load(img_feature_path+img_id+'.npy')
img_tensor_val=tf.reshape(img_feat, (1, img_feat.shape[0], img_feat.shape[1]))
dec_input = tf.expand_dims([tokenizer.word_index['<start>']], 0)
result = []
for i in range(max_len):
_, combined_mask, _ = create_masks(img_tensor_val, dec_input)
predictions, attention_weights = transformer(img_tensor_val, dec_input, False, None, combined_mask, None)
predictions = predictions[: ,-1:, :]
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
pred_id_int = predicted_id.numpy()[0][0]
result.append(tokenizer.index_word[pred_id_int])
if tokenizer.index_word[pred_id_int] == '<end>':
return result
dec_input = tf.concat([dec_input, predicted_id], axis=-1)
return result
#beam search algorithm
def bs_evaluate(test_img_n):
img_id = test_img_n.split('/')[-1]
img_feat= np.load(img_feature_path+img_id+'.npy')
img_tensor_val=tf.reshape(img_feat, (1, img_feat.shape[0], img_feat.shape[1]))
captions = [['<start>', 0.0]]
for i in range(max_len):
all_cap=[]
for cap in captions:
sentence, score = cap
if sentence.split()[-1] == '<end>':
all_cap.append(cap)
continue
dec_input = tokenizer.texts_to_sequences([sentence])
_, combined_mask, _ = create_masks(img_tensor_val, dec_input)
predictions, img_att = transformer(img_tensor_val, tf.cast(dec_input, tf.int16), False, None, combined_mask, None)
predictions = predictions[: ,-1:, :]
pred_arg = tf.cast(tf.argsort(predictions, axis=-1), tf.int32)
pred_args = pred_arg.numpy()[0][0][-3:]
for pred_id in pred_args:
word = tokenizer.index_word[pred_id]
caption = [sentence+ ' '+ word, score + predictions[0][0][pred_id].numpy()]
all_cap.append(caption)
ordered = sorted(all_cap, key = lambda tup: tup[1], reverse=True)
captions = ordered[:3]
return captions
test_dict = load(open(data_path+'/data_preprocess/sc_test_dict.p',"rb"))
test_imgs = list(test_dict.keys())
import json
# storing the json file for predicted ana actual report.
actual_= []
pred = []
pred_1 = []
pred_2 = []
pred_3 = []
img_id = 0
for img in test_imgs:
pred_bs_caption = bs_evaluate(img)
pred_capt = '<start> '+' '.join(feat_evaluate(img))
actual_.append({"image_id":img_id, "caption":test_dict[img]})
pred.append({"image_id":img_id,"caption":pred_capt})
pred_1.append({"image_id":img_id,"caption":pred_bs_caption[0][0]})
pred_2.append({"image_id":img_id,"caption":pred_bs_caption[1][0]})
pred_3.append({"image_id":img_id,"caption":pred_bs_caption[2][0]})
img_id+=1
#print('-caption completed- :', img_id)
coco_actual_cap={'info': {
'description': None,
'url': None,
'version': None,
'year': None,
'contributor': None,
'date_created': None,
},
'images':[
{
'license':None,
'file_name':None,
'id':image_id,
'width':None,
'date_captured':None,
'height':None
}
for image_id in range(len(actual_))
],
'licenses':[
],
'type':'captions',
'annotations':[
{
'image_id':actual_[i]["image_id"],
'id':j,
'caption':actual_[i]["caption"][j]
}
for i in range(len(actual_)) for j in range(len(actual_[i]["caption"]))
]
}
with open("/home/gaurav/TF2/F30K/coco-caption/annotations/SC_actual_test_cap.json","w", encoding='utf-8') as AC:
print(str(json.dump(coco_actual_cap, AC)))
with open("/home/gaurav/TF2/F30K/coco-caption/predictions/SC_pred_test_cap.json","w") as PC:
json.dump(pred, PC)
with open("/home/gaurav/TF2/F30K/coco-caption/predictions/SC_bs1_test_cap.json","w") as PC:
json.dump(pred_1, PC)
with open("/home/gaurav/TF2/F30K/coco-caption/predictions/SC_bs2_test_cap.json","w") as PC:
json.dump(pred_2, PC)
with open("/home/gaurav/TF2/F30K/coco-caption/predictions/SC_bs3_test_cap.json","w") as PC:
json.dump(pred_3, PC)